Sushant Choudhary

If Apache Iceberg adoption is fast, the ecosystem around it is growing even faster and that's creating one of the cheapest lakehouse infrastructure options available today To support this ecosystem, we have ClickHouse with its recent updates and how it is shaping our stack of choice . The Cost-Effective Stack: OLake by Datazip + Apache Iceberg + ClickHouse Here's what I'm seeing teams build for maximum cost efficiency: OLake, handles the heavy lifting of real-time database replication - streaming from PostgreSQL, MySQL and MongoDB directly into Apache Iceberg with throughput of 46K+ records/second. It's open-source, requires minimal infrastructure (no Spark, no Flink, no Debezium), and supports all major Iceberg catalogs. Not to state the hero of the stack- Iceberg provides the open table format that eliminates vendor lock-in while delivering ACID transactions, schema evolution, and time travel capabilities. ClickHouse delivers the analytics layer with proven 5-15x cost advantages over traditional warehouses but what I want to highlight are it's recent updates . ClickHouse v25.8 dropped major updates for Iceberg support (full source in comments) : ✅ Native Write Support - Full CRUD operations, not just reads ✅ Production-Ready Catalogs - REST, Glue, Unity all promoted to beta ✅ Schema Evolution - Add/drop/modify columns seamlessly ✅ Better Deletes - Position deletes merged efficiently ✅ Near Real-time Streaming - Perfect for ingestion platforms like OLake Why This Stack Works: Real-time ingestion → Open storage → Fast analytics = Maximum performance at minimum cost. #ApacheIceberg #OpenTableFormats #ClickHouse #DataLakehouse

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Many AI teams are scaling LLM inference workloads on-prem for security, performance, and cost savings. ✅ Most will start with: 1. Infrastructure layer: Servers, GPUs, networking, storage, power, cooling, etc. 2. Container orchestration layer: K8s/OpenShift to manage and schedule workloads. ⚠️ What’s often missing? A 𝗽𝘂𝗿𝗽𝗼𝘀𝗲-𝗯𝘂𝗶𝗹𝘁 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗹𝗮𝘆𝗲𝗿 for LLM inference and agentic systems, the overlooked layer where things often fall short: - Slow time to market - Poor GPU utilization and cost visibility - Performance bottlenecks - Lack of LLM-specific observability - High operational overhead In our latest post, we break down the modern on-prem LLM stack and why the inference platform layer is a critical piece. 👉 Learn more: https://lnkd.in/gBFisHZJ

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AWS just launched 𝐄𝐂𝐒 𝐌𝐚𝐧𝐚𝐠𝐞𝐝 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞𝐬 🎉 You still get EC2 flexibility (instance families, GPUs, Graviton, Spot, custom AMIs/agents), but AWS handles the rest of noise, aka infrastructure management responsibilities. - regular security patches initiated every 14 days  - flexibility in instance type selection - multiple ECS tasks running on the same instance by default (unlike Fargate’s one task per VM model) - the service runs on Bottlerocket OS and probably more. Read all the details and try it out: https://lnkd.in/ewg-sud5 Curious how you’d use it ? Share your take or use case in the comments ✍🏼 ♻️ If this was helpful, please repost. 👩🏻‍💻 Follow me (Kristine Armiyants) to stay in the loop with the latest AWS updates and community activities. #amazon #awshero #awsheroes #awsugarm #awsusergroups  #awsusergroup #awscommunity #awscommunitybuilders  #awscommunitybuilder  #awsusergrouparmenia #cloudengineering  #careergrowth  #careerdevelopment #communitymanagement #leadership #armenia #techarmenia #armeniatech #fargate #ec2 #ecs

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